New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered Evolution

TL;DR

This paper introduces GAPE, a genetic algorithm-based method for optimizing deep learning models to improve energy, position estimation, and classification of reactor antineutrino interactions in PROSPECT, enhancing signal detection accuracy.

Contribution

The paper presents GAPE, a novel genetic algorithm approach for optimizing deep learning models specifically for reactor neutrino data analysis, outperforming traditional methods in some cases.

Findings

GAPE-optimized models can outperform traditional PROSPECT models.

Classifier improves signal-to-background ratio by nearly 2.8 times.

Biases in classifier validation can be mitigated with data-period-specific training.

Abstract

We propose a genetic algorithm powered evolution (GAPE) method to create deep learning solutions for energy and position estimation for reactor antineutrino interactions in the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT) at the highly enriched High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. We also apply GAPE to create classification models to distinguish signatures of inverse beta decay (IBD) interactions of reactor antineutrinos from common background types. The GAPE method can also be adopted for optimization of other types of problems that utilize machine learning (ML) models for particle physics applications. When applied in the PROSPECT context, we find that the models selected by GAPE can, in some cases, outperform the traditional models previously used for PROSPECT data analysis. In particular, when benchmarked against conventional PROSPECT neutrino identification pathways using the same underlying information, the classifier offers the promise of improving the signal-to-background ratio by nearly 2.8 times. Performance biases uncovered during initial IBD classifier validation were primarily caused by differences in time-dependent response between background and signal training datasets. Biases were effectively mitigated through a data-period-specific training regimen, offering a pathway towards realizing an unbiased IBD signal classifier for future reactor neutrino datasets.